ai-pong-player

Pong Game AI using NEAT

This repository contains a Python implementation of the classic game Pong with an AI that learns to play the game using NEAT (NeuroEvolution of Augmenting Topologies). NEAT is a genetic algorithm-based method for evolving artificial neural networks to solve various tasks, including playing games.

Getting Started

To get started, follow the steps below:

1. Clone the repository to your local machine.
2. Install the required dependencies using pip install -r requirements.txt.

How It Works

The pong.py file contains the game logic for Pong, including the ball, paddles, and game loop. The neat library is used to implement the NEAT algorithm for evolving neural networks.

• PongGame class: This class represents the Pong game environment and contains methods for testing the AI against a human player (test_ai) and training the AI (train_ai) by playing two neural networks against each other.
• eval_genomes function: This function runs each genome against each other one time to determine their fitness.
• run_neat function: This function sets up the NEAT population, runs the evolution process using eval_genomes, and saves the best-performing neural network to a pickle file.
• test_best_network function: This function loads the best-performing neural network from the pickle file and tests it against a human player.

Configuration

The config.txt file contains the NEAT configuration parameters, including population size, mutation rates, and species settings. You can adjust these parameters to experiment with different evolutionary settings.

Contributing

If you would like to contribute to this project or report any issues, feel free to open a pull request or create an issue on the GitHub repository.

Credits

This project uses the NEAT library (https://neat-python.readthedocs.io/) for implementing the NEAT algorithm.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.